Current approaches taken towards this can be broadly classified into (a) those using rule-based generation of visualizations [1]}, [2]} and (b) those based on machine models learnt on a large pool of existing visualizations [3]}, [4]}. Unfortunately, both these approaches suffer from serious limitations: on one hand, the rule-based methods have the obvious limitation that they can not easily adapt to new data or visualization paradigms. On the other hand, the machine learnt models rely on the availability of a large volume of prior data and its visualization to train on, making them hard to easily adapt to new visualizations. For example, the latest VizML system [4]} uses more than 2.3 million data-visualization pairs to train, making it harder to replicate and highly dependent on the quality of visualizations chosen by the crowd. Apart from this, they also are affected by the (un)availability of certain visualizations in the training corpus. Further, neither approach considers explicitly quantifying how well the generated / recommended plot performs in terms it capturing the underlying data.
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